Resolving and understanding the genomic basis of heterogeneous complex traits and diseases

父母补助金 解决和理解异质复杂性状和疾病的基因组基础。 数以百计的基因组学研究已经暴露了我们在理解这些机制关系方面的主要差距 基因组变异、细胞过程、组织功能和性状变异之间的联系。家长的目标 项目是开发一套计算框架，整合大量的基因组和 生物医学数据，使以下三个方面的进展： 方向1：识别和利用复杂性状和疾病的基于机制的亚型。 方向2：表征生理和疾病沿着人类寿命和跨性别。 方向3：在研究人类特征/疾病的模式生物中找到类似的背景。 正如我们和其他人所证明的那样，全基因组分子网络是分子数据的大统一者， 知识，并作为强有力的工具，上下文理解基因在细胞通路中发挥的作用， 组织生理学、表型/疾病机制和药物作用。因此，我们父母的一个核心方面 项目是开发多种机器学习方法，利用分子网络生成 关于基因在定义亚型、年龄/性别差异和 一系列复杂疾病的跨物种类似物。 作为这项工作的一部分，我们开发了GenePlexus github.com/krishnanlab/GenePlexus，一个开源的 软件运行和基准我们的国家的最先进的方法相结合的基因组规模的网络与 监督机器学习（ML）以获得关于各种基因属性的准确的新颖预测（例如， 途径成员或疾病关联; Liu*，Mancuso*，等人，2020生物信息学）。 同样，我们的团队也致力于将我们所有其他的计算方法推广到更广泛的领域。 生物医学研究社区的开放科学的软件工具的形式。我们已经发布了这样的 几乎所有的文件都是软件。最近的其他例子包括： ● PecanPy github.com/krishnanlab/PecanPy，用于并行化、高效和加速的node 2 vec。 ● Expesto github.com/krishnanlab/Expresto，用于估算转录组中未测量的基因。 ·Txt 2 Onto github.com/krishnanlab/Txt2Onto，用于基于自由文本元数据注释组学样本。 补充项目的目标和当前原型 GenePlexus：基于网络的机器学习云平台 建议补充的目标是通过构建一个新的 基于云的GenePlexus平台，使：i）生物医学/实验研究人员能够无缝地 基于网络的ML生成可解释的全基因组预测的优势，以及ii）计算 研究人员可以运行基于网络的机器学习，检索结果，并与现有的数据分析工作流程集成。 该项目团队，其中包括PI，在云计算培训博士后，和两个专业软件 在过去的六个月里， (in安装在Microsoft Azure上的Web服务器的形式;图1）。 图1：当前 GenePlexus网络服务器的原型， 实现了我们最初的GenePlexus软件 github.com/krishnanlab/GenePlexus.当一个 用户上传基因集，GenePlexus创建 训练ML模型的虚拟机， 利用预先保存的数据。除了 从功能上预测网络中的基因 类似于用户提供的基因， 使用户能够解释 根据其相似性定制ML模型 成千上万的模型， 预测与生物学相关的基因 过程（来自基因本体论）和疾病 (from DisGeNET）。除了检索 预测结果在多个方便 格式，用户可以可视化的顶部预测 基因作为网络图。

Parent grant Resolving and understanding the genomic basis of heterogeneous complex traits and diseases. Hundreds of genomics studies have exposed major gaps in our understanding of the mechanistic relationships between genomic variation, cellular processes, tissue function, and trait variation. The goal of the parent project is to develop a suite of computational frameworks that integrate massive collections of genomic and biomedical data to make the following three advances: Direction 1: Discern and leverage mechanism-based subtypes of complex traits and diseases. Direction 2: Characterize physiology and disease along the human lifespan and across the sexes. Direction 3: Find analogous contexts in model organisms for studying human traits/diseases. As demonstrated by us and others, genome-wide molecular networks are grand unifiers of molecular data and knowledge, and serve as powerful tools to contextually understand the roles genes play in cellular pathways, tissue physiology, phenotype/disease mechanisms, and drug action. Hence, a central aspect of our parent project is to develop multiple machine learning approaches to leverage molecular networks to generate accurate, testable hypotheses about the roles genes play in defining subtypes, age/sex differences, and cross-species analogs of a range of complex disorders. As part of this work, we have developed GenePlexus github.com/krishnanlab/GenePlexus, an open source software to run and benchmark our state-of-the-art approach for combining genome-scale networks with supervised machine learning (ML) to get accurate novel predictions about various gene attributes (e.g., pathway membership or disease association; Liu*, Mancuso*, et al., 2020 Bioinformatics). Similarly, our group has committed efforts to make all our other computational methods available to the broader biomedical research community in the form of software tools for open science. We have released such software with nearly all our papers. Other recent examples include: ● PecanPy github.com/krishnanlab/PecanPy for parallelized, efficient, and accelerated node2vec. ● Expresto github.com/krishnanlab/Expresto for imputing unmeasured genes in transcriptomes. ● Txt2Onto github.com/krishnanlab/Txt2Onto for annotating –omics samples based on free-text metadata. Goal of the supplement project and current prototype GenePlexus: A cloud platform for network-based machine learning The goal of the proposed supplement is to take our software development to the next level by a building a new cloud-based GenePlexus platform to enable: i) biomedical/experimental researchers to seamlessly take advantage of network-based ML to generate interpretable genome-wide predictions, and ii) computational researchers to run network-based ML, retrieve results, and integrate with existing data analysis workflows. The project team, which includes the PI, a postdoc trained in cloud computing, and two professional software engineers – has worked together over the past six months to develop a prototype of the GenePlexus platform (in the form of a web-server mounted on Microsoft Azure; Fig. 1). Figure 1: Screenshots of the current prototype of the GenePlexus webserver that implements our original GenePlexus software github.com/krishnanlab/GenePlexus. When a user uploads a geneset, GenePlexus creates a virtual machine that trains a ML model, leveraging pre-saved data. In addition to predicting genes in the network functionally similar to the user-supplied genes, GenePlexus provides enables the user to interpret the custom-built ML model in terms of its similarity to thousands of models that were trained to predict genes associated with biological processes (from Gene Ontology) and diseases (from DisGeNET). In addition to retrieving the prediction results in multiple convenient formats, users can visualize the top predicted genes as a network graph.